A Vision-Based Automatic Landing Method for Fixed-Wing UAVs

In this paper, a vision-based landing system for small-size fixed-wing unmanned aerial vehicles (UAVs) is presented. Since a single GPS without a differential correction typically provide position accuracy of at most a few meters, an airplane equipped with a single GPS only is not guaranteed to land at a designated location with a sufficient accuracy. Therefore, a visual servoing algorithm is proposed to improve the accuracy of landing. In this scheme, the airplane is controlled to fly into the visual marker by directly feeding back the pitch and yaw deviation angles sensed by the forward-looking camera during the terminal landing phase. The visual marker is a monotone hemispherical airbag, which serves as the arresting device while providing a strong and yet passive visual cue for the vision system. The airbag is detected by using color- and moment-based target detection methods. The proposed idea was tested in a series of experiments using a blended wing-body airplane and proven to be viable for landing of small fixed-wing UAVs.     

A Vision-Based Particle Tracking Velocimetry

Particle Image Velocimetry (PIV) is a non-intrusive optical technique to measure velocity of flows. It provides the simultaneous visualization of the streamline pattern in unsteady flows and the quantification of the velocity field over the image plane. To reveal the flow motion, the flow is seeded by small scattering particles. The instantaneous fluid velocities are evaluated by recording the images of tracers, suspended in the fluid and traversing a light sheet. A PIV system consists of seeding particles, illumination unit, image acquisition system, and a computer for image processing. For industrial applications a classical PIV system is not suitable for its cost, sizes and needs of specialised users and work areas. Moreover, classical PIV are unable to work in real-time for the huge amount of data and expensive algorithms adopted. In this paper, a study and the implementation of a new PIV system is described and compared against classical PIV solutions. The solution proposed is capable of working in real-time and is a Continuous PIV, CPIV, system: with respect to classical PIV, it is composed of a continuous laser light source and a CCD camera. A specifically new image-processing algorithm for velocity estimation and recognition of correct traces has been developed. It is based on the grey level distribution in the particle trace image, and indicates those particles moving with an out-of-plane velocity vector component and provides the measure with a limited error.     

用于机器人轨迹跟踪的结构控制

实际机器人控制过程中,不确定参数及外部干扰几乎总是存在的,研究考虑了这些因素的机器人控制问题,在理论及应用中都有重要意义。本文考虑了存在外扰及不确定参数时,机器人的轨迹跟踪问题,利用变结构控制理论,结合机器人自身的结构特点,研制了两组控制规律,它一方面对外扰不敏感,另方面不要求精确的机器人参数,也勿需对参数辨识,因而运算简单,易于实现,理论分析及仿真试验都证明了所给控制能保证渐近轨迹跟踪。     

Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

This paper proposes vision-based techniques for localizing an unmanned aerial vehicle (UAV) by means of an on-board camera. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. First, it is described a monocular visual odometer which could be used as a backup system when the accuracy of GPS is reduced to critical levels. Homography-based techniques are used to compute the UAV relative translation and rotation by means of the images gathered by an onboard camera. The analysis of the problem takes into account the stochastic nature of the estimation and practical implementation issues. The visual odometer is then integrated into a simultaneous localization and mapping (SLAM) scheme in order to reduce the impact of cumulative errors in odometry-based position estimation approaches. Novel prediction and landmark initialization for SLAM in UAVs are presented. The paper is supported by an extensive experimental work where the proposed algorithms have been tested and validated using real UAVs.     

Vision-Based Fixed-Time Uncooperative Aerial Target Tracking for UAV

<正>Dear Editor, In this letter, a vision-based fixed-time control is proposed for an unmanned aerial vehicle(UAV) with actuator saturation to track an uncooperative target. The fixed-time control is designed in backstepping framework. The relative states between UAV and the target are not directly measured, and are estimated by an onboard monocular camera.     

Incremental Focus of Attention for Robust Vision-Based Tracking

We present the Incremental Focus of Attention (IFA) architecture for robust, adaptive, real-time motion tracking. IFA systems combine several visual search and vision-based tracking algorithms into a layered hierarchy. The architecture controls the transitions between layers and executes algorithms appropriate to the visual environment at hand: When conditions are good, tracking is accurate and precise; as conditions deteriorate, more robust, yet less accurate algorithms take over; when tracking is lost altogether, layers cooperate to perform a rapid search for the target and continue tracking.Implemented IFA systems are extremely robust to most common types of temporary visual disturbances. They resist minor visual perturbances and recover quickly after full occlusions, illumination changes, major distractions, and target disappearances. Analysis of the algorithm's recovery times are supported by simulation results and experiments on real data. In particular, examples show that recovery times after lost tracking depend primarily on the number of objects visually similar to the target in the field of view.     

基于视觉的多点触摸基本技术实现方法

高性能的图像处理技术是基于视觉的多点触摸技术的关键,对于特征点的检测和跟踪是完成多点触摸系统的基础.文中主要介绍了几种算法用来解决特征点的识别和跟踪的问题.首先,图像轮廓变换算法用来分析手指与屏幕的接触区域,以及在此基础上采用中心计算算法确定特征点的中心坐标;再次,最小距离优先(MDF)算法对在两个相邻图像中的相应的特征点进行识别和跟踪,为随后的事件检测和手势识别提供了前提条件.实验结果表明,算法的性能满足多点触摸系统的实时要求.     

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.     

A Catadioptric and Pan-Tilt-Zoom Camera Pair Object Tracking System for UAVs

Unmanned aerial vehicles (UAVs) are seeing widespread use in military, scientific, and civilian sectors in recent years. As the mission demands increase, these systems are becoming more complicated. Omnidirectional camera is a vision sensor that can captures 360° view in a single frame. In recent years omnidirectional camera usage has experienced a remarkable increase in many fields, where many innovative research has been done. Although, it is very promising, employment of omnidirectional cameras in UAVs is quite new. In this paper, an innovative sensory system is proposed, that has an omnidirectional imaging device and a pan tilt zoom (PTZ) camera. Such a system combines the advantages of both of the camera systems. The system can track any moving object within its 360° field of view and provide detailed images of it. The detection of the moving object has been accomplished by an adaptive background subtraction method implemented on the lowered resolution images of the catadioptric camera. A novel algorithm has also been developed to estimate the relative distance of the object with respect to the UAV, using tracking information of both of the cameras. The algorithms are implemented on an experimental system to validate the approach.     

一种分层Mean Shift目标跟踪算法

针对经典Mean shift (MS)目标跟踪算法的颜色特征鲁棒差、匹配迭代复杂的缺点, 提出一种分层Mean shift (Hierarchical mean shift, HMS)目标跟踪算法. 首先通过MS迭代将目标区域特征空间的数据点聚类于模式点, 使得以简洁的方式描述前景跟踪目标, 建立目标模型与目标候选模型的聚类模式点描述, 进行聚类块匹配. 然后, 导出聚类块模式点匹配下的相似度量函数, 进行像素点匹配, 结合邻域一致性, 计算像素平移量, 分层估计序列帧中跟踪目标质心模式点的位置, 并给出HMS匹配迭代跟踪算法. 实验结果表明, 与其他两种MS跟踪算法相比, HMS既能提高序列帧跟踪目标表达与匹配的鲁棒性, 又无需匹配所有数据点, 算法简洁且有效可行.     

Multiresolution Hierarchical Path-Planning for Small UAVs Using Wavelet Decompositions

We present an algorithm for solving the shortest (collision-free) path planning problem for an agent (e.g., a small UAV) with limited on-board computational resources. The agent has detailed knowledge of the environment and the obstacles only in the vicinity of its current position. Far away obstacles are only partially known and may even change dynamically. The algorithm makes use of the wavelet transform to construct an approximation of the environment at different levels of resolution. We associate with this multiresolution representation of the environment a graph, whose dimension can be made commensurate to the on-board computational resources of the agent. The adjacency list of the graph can be efficiently constructed directly from the approximation and detail wavelet coefficients, thus further speeding up the whole process. Simulations are presented to test the efficiency of the algorithm using non-trivial scenarios.     

Sky Region Obstacle Detection and Tracking for Vision-Based UAS Sense and Avoid

A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two main steps. Specifically, the first processing step is relevant to obstacle detection and tentative tracking for track confirmation and is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. An extensive experimental analysis is presented which is based on a large set of flight data gathered in realistic near collision scenarios, in different operating conditions in terms of weather and illumination, and adopting different navigation units onboard the ownship. In particular, the focus is set on flight segments at a range between 3 km and 1.3 km, since the major interest is in understanding algorithm potential for relatively large time to collision. System performance is evaluated in terms of declaration range, probability of correct declaration, average number of false positives, tracking accuracy (angles and angular rates in a stabilized North-East-Down reference frame) and robustness with respect to track loss phenomena. Promising results are achieved regarding the trade-off between declaration range and false alarm probability, while the onboard navigation unit is found to heavily impact tracking accuracy.     

A Picking Strategy for Circular Conveyor Tracking

In modern assembly lines, one of the most relevant issues is the automatic part feeding. This task consists in the separation of parts delivered in bulk and their presentation in a certain amount and orientation at the pick-up location. The traditional vibratory bowl feeder can no longer be adopted for the modern plants which require high velocity and increasing flexibility. In this paper, several types of feeders have been indentified and the Ars FlexiBowl has been classified as one of the most innovative. In order to take full advantage from this rotating feeder, a proper path planning technique is proposed for a generic manipulator. Such an approach consists mainly of two algorithms: one for prediction of moving part position, and one for circular conveyor tracking. These algorithms can be implemented into the robot control unit by exploiting the internal trajectory planner. This strategy has been implemented into an Adept robotic workcell, which has been adopted as an experimental setup. Such an application proves the usefulness of the proposed algorithms.     

四旋翼无人机参数预测和抗扰动自适应轨迹跟踪控制

为了降低外界环境对四旋翼无人机飞行轨迹的扰动性,提高无人机的控制精度,提出1种基于滑模控制的四旋翼无人机参数预测和抗扰动的自适应轨迹跟踪控制器。这种控制器对四旋翼无人机系统的不确定状态参数、气流、风阻和执行器故障等外界扰动进行预测,实现了对系统输入的状态补偿和扰动补偿,提高了无人机的轨迹跟踪效率和抗扰动能力,消除了机体在飞行过程中的抖振现象,提高了无人机系统对环境的适应性和控制器的稳定性。通过仿真实验,分析了四旋翼无人机在不同控制器作用下的轨迹跟踪性能曲线,验证了所提出的控制器的优越性和有效性。     

Real-Time Target Tracking for Autonomous UAVs in Adversarial Environments: A Gradient Search Algorithm

This paper presents a rule-based intelligent guidance strategy for autonomous pursuit of mobile targets by unmanned aerial vehicles (UAVs) in an area with threats, obstacles, and restricted regions. The probabilistic threat exposure map (PTEM) is used as the mathematical formulation of the area of operation for the guidance strategy to make intelligent decisions based on a set of defined rules. The rules are developed for three objectives in the order of priority as: 1) avoid obstacles/restricted regions; 2) maintain the target proximity; 3) minimize UAV threat exposure level. A least-square estimation and kinematic relations are used to estimate/predict the target states based on noisy position measurements. The work presented herein addresses the same problem as in a previous work by the authors, and aims at improving the computational efficiency without compromising the performance. Simulation results of several pursuit scenarios demonstrate the full capabilities of the strategy and the improvement over the previous work     

A 3D Collision Avoidance Strategy for UAVs in a Non-Cooperative Environment

This paper presents a feasible 3D collision avoidance approach for fixed-wing unmanned aerial vehicles (UAVs). The proposed strategy aims to achieve the desired relative bearing in the horizontal plane and relative elevation in the vertical plane so that the host aircraft is able to avoid collision with the intruder aircraft in 3D. The host aircraft will follow a desired trajectory in the collision avoidance course and resume the pre-arranged trajectory after collision is avoided. The approaching stopping condition is determined for the host aircraft to trigger an evasion maneuver to avoid collision in terms of measured heading. A switching controller is designed to achieve the spatial collision avoidance strategy. Simulation results demonstrate that the proposed approach can effectively avoid spatial collision, making it suitable for integration into flight control systems of UAVs.     

Real-Time Cutting Path Determination Using Machine Vision-Based Incremental Pattern Tracking

In this article a fundamentally new approach to cutting patterned breadths of lace fabric from a mesh backing web is presented. A line scan camera-based vision system employing a special tracking algorithm is used to detect pattern registration of lace fabric incrementally, allowing real-time determination of the local cutting path in accordance with a pre-defined reference map. This approach achieves high tracking speeds and a high level of robustness. The determined cutting path data is used to target a CO₂laser beam actively to achieve separation of lace ribbon at speeds of 1 m/sec. The new machine which utilizes this vision guidance system provides levels of production that have been hitherto unattainable with conventional mechanically based lace cutting machines.     

一种新的机械手轨迹跟踪鲁棒控制

本文考虑存在外扰及参数不确定情况下机械手轨迹跟踪问题，通过利用机械手固有的结构特性，提出一种基于李雅普诺夫方法的新控制算法．该算法的新颖之处在于无需预先知道参数的不确定范围，并且减弱了对外扰的限制．该算法的渐近跟踪性已被证明．